production-analytics/README.md

# production-analytics

`production-analytics` is a small Python service that derives production
analytics from ENLYZE data for Grafana. ENLYZE remains the authority for raw
process data; this project persists only derived metrics, events, relevant
production-order context, and calculation state.

## Status

This repository includes an ENLYZE exploration CLI, a production-run/timeseries
gateway, and a configured continuous material polling runner with atomic JSON
checkpoints and PostgreSQL cumulative material-consumption snapshots for Grafana.
The live material-consumption MVP remains in progress.
There are no HTTP endpoints.

## Intended flow

```text
ENLYZE (raw data) -> retrieval boundary -> calculations -> TimescaleDB -> Grafana
                                      \-> calculation provenance/state
```

Production orders are the primary attribution context for results. Every
persisted result will ultimately be traceable to its machine, order/article
context, source time range, and calculation implementation version.

## Development

Requires Python 3.11 or newer. Install the project and development tools:

```bash
python3 -m pip install -e '.[dev]'
pytest
ruff check .
```

Copy `.env.example` to `.env` for public/local configuration documentation.
Put real ENLYZE credentials only in `secrets/enlyze.env`, which is ignored.
The CLI reads that file by default without executing it; it accepts only simple
`KEY=VALUE` lines (quoted values are supported). Environment variables may be
used instead when appropriate. Never pass keys as command-line arguments.

The generic request command only performs `GET` requests and requires the
operator to provide an API path that is known to be safe and authorized:

```bash
production-analytics enlyze raw /verified/path --pretty
production-analytics enlyze raw /verified/path --save-fixture response.json
```

Public ENLYZE documentation verifies Bearer-token authentication. Put
`ENLYZE_API_KEY='...'` in the local secret file; the exploration client sends
it as an `Authorization: Bearer …` header and never prints that header/value.

The second command saves a sanitized, reviewable fixture under
`fixtures/enlyze/` by default. Raw captures belong in the ignored
`data/raw/enlyze/` directory and must not be committed.

The OpenAPI server URL is `https://app.enlyze.com/api/`; its operation paths
begin with `/v2/`. Set `ENLYZE_BASE_URL` to that server URL, not to an
operation path. The documented read-only time-series operation is exposed for
exploration as `production-analytics enlyze timeseries`.

The current Compose file has no application service, so it deliberately does
not pass ENLYZE credentials to TimescaleDB. A future application service should
use `env_file: ./secrets/enlyze.env` rather than copying secrets into Compose.

## Material-consumption integration

`MaterialConsumptionIntegrator` in `calculations` accepts `MaterialSample`
values (timezone-aware timestamp, material rate in kg/h, numeric gate value).
Configure a strict `gate_value > gate_threshold` condition and an explicit,
positive `max_sample_gap_seconds`. Each interval uses the preceding sample's
rate and gate, converting elapsed seconds to hours to accumulate kg. A longer
gap contributes neither consumption nor running time and resets the baseline
to the newer sample. No time before the first or after the last sample is inferred.

Call `process(sample)` or `process_many(samples)` on the same instance for live
input, replay, or successive chunks. Both use the same calculation. The immutable
`state` snapshot exposes cumulative kg, integrated running seconds, last timestamp
(UTC), last rate, last gate, and whether the latest gate is active. State restoration
and JSON persistence are supported. Naive timestamps and non-finite values
are rejected; backwards timestamps raise without changing state. Duplicate
timestamps add no consumption but replace the baseline in arrival order.
Finite negative material rates are currently accepted and decrease cumulative
consumption during affected integrated intervals. This is intentional generic
behavior for now; machine-specific validation or clamping may be added later
at the input/adapter layer if required by process semantics.

Run `python scripts/validate_k7_material_consumption.py` from the repository root
with the ignored `k7-00842-throughput.raw.json` and `k7-00842-speed.raw.json`
captures in `data/raw/enlyze/`. The utility joins common non-null timestamps
without filling values and rejects unordered or duplicate capture records.
K7-specific signal UUIDs are declared in the runner configuration and validation
utility: `Stundenleistung Anlage` is gated by `Geschwindigkeit Gesamtanlage > 0.5 m/min`. With the explicit
20-second validation gap limit (`--max-sample-gap-seconds` to override),
2,672 common samples yield **5.205555556 h** and **5,180.127150811 kg** for run
00842, matching the previous manual calculation's rounded results. Synthetic
tests verify equivalence to manual interval integration without local captures
or live ENLYZE access. Grafana and Bento 1 remain pending.

`MaterialPollingService.poll_once(now=...)` starts at the open run's start or
resumes inclusively at its last processed timestamp. Duplicate boundary samples
add no time. A new run UUID for the same order retains cumulative kg and running
seconds but resets the temporal baseline; different orders and machines have
separate state. Empty responses add no consumption. Naive `now` is rejected;
no open run or a window beginning after `now` returns `None` without saving.

`JsonMaterialStateStore` uses deterministic SHA-256 filenames derived from the
machine/order pair, preventing path traversal and sanitized-name collisions.
It flushes and fsyncs a temporary file in the state directory before atomic
replacement; a failed write leaves the previous primary file intact. Missing
state returns `None`; malformed JSON or state raises `ValueError` with file
context. Use an ignored runtime directory and one polling writer per state key;
atomic replacement does not coordinate concurrent read-modify-write cycles.
Files from the earlier uncommitted sanitized-filename prototype are not loaded
under the new names. The gateway rejects ambiguous open runs, naive windows,
and missing columns or malformed records instead of silently skipping them.

## Continuous material polling

The committed [K7 configuration](config/k7-material-consumption.yaml) declares
`k7-fiber-consumption`, a `material_consumption` calculation at version `"1"`.
It selects `Stundenleistung Anlage` (kg/h) as the rate and `Geschwindigkeit
Gesamtanlage` (m/min) as the gate, integrating only when the gate is **strictly
above 0.5**, with a maximum sample gap of 20 seconds. `Anlage läuft` is not the
primary gate. The output is `material_consumption` in `kg`, grouped by
`production_order`; production orders remain opaque strings, including leading
zeros and whitespace.

Install the declared dependencies in the repository venv, then run from the
repository root:

```bash
.venv/bin/python -m pip install -e '.[dev]'
.venv/bin/python -m production_analytics.cli run material-poll \
  --config config/k7-material-consumption.yaml \
  --calculation-id k7-fiber-consumption \
  --poll-interval-seconds 10 \
  --state-directory data/state/material \
  --secrets-file secrets/enlyze.env
```

The installed `production-analytics run material-poll` command is equivalent.
The interval, state directory, and secrets path above are defaults. Paths are
relative to the current working directory. Existing `ENLYZE_BASE_URL`,
`ENLYZE_API_KEY`, and `ENLYZE_HTTP_TIMEOUT_SECONDS` handling is reused; values
in the secrets file override environment values. Keys are never CLI arguments.

Keep calculation fields in YAML and technical runtime settings in CLI options.
The loader requires every field shown in the K7 file, rejects unknown fields,
duplicate keys/ids, unsupported types/versions/outputs, and invalid or non-finite
numbers. Numeric fields must be YAML numbers, and `version` must be quoted.
Multiple material calculations may share a file, but then `--calculation-id` is
required. All entries are validated before selection. The unrelated
`config/calculations.example.yaml` remains illustrative and is not executable
by this material-only runner.

The foreground runner polls once immediately using UTC, then sleeps for the
configured positive interval after each completed cycle, including failures.
A slow poll delays the next cycle; there is no overlap or catch-up scheduling.
Ctrl-C/SIGINT exits cleanly. Successful cycles print machine, opaque order,
cumulative kg, integrated running seconds, and cycle-start timestamp. No open
run (or no eligible window) is normal and visible. Errors on stderr identify
state loading/saving or ENLYZE/gateway/polling and the exception class; arbitrary
exception text and response bodies are withheld to avoid leaking credentials.
Startup/configuration failures return non-zero; cycle failures retry after the
delay without replacing checkpoints with empty state.

Checkpoints live in ignored `data/state/material/` by default, with one hashed
JSON filename per machine/order pair. Run **one polling writer per state key**;
there is no multi-process locking. Use a separate state directory when changing
calculation parameters or running a different calculation for the same machine
and order: checkpoints are not namespaced by calculation id/version. These files
are integration checkpoints, not a Grafana metric store.

Each successful non-empty poll writes one cumulative snapshot to PostgreSQL before
printing its result. JSON remains the restart checkpoint; PostgreSQL stores derived
time-series snapshots only. ENLYZE remains the raw-data source of truth. Database
write failures emit a concise error and polling continues with the next cycle;
missed snapshots are not retried or backfilled and JSON state is not rolled back.
The next successful snapshot includes the continuing cumulative total.

A new order's first snapshot is **not guaranteed to be zero**: the service processes
available samples from the run start before returning, so its first total may
already be non-zero. A returned zero is stored normally. Totals continue across
runs for the same order; existing checkpoints for a previously seen order resume.

### PostgreSQL setup

Set all five runtime settings: `POSTGRES_HOST`, `POSTGRES_PORT`, `POSTGRES_DB`,
`POSTGRES_USER`, and `POSTGRES_PASSWORD`, using exported environment variables or
the existing secrets file (file values override the environment). The runner does
not automatically load `.env`. Missing or invalid settings fail at startup;
connectivity and schema errors are reported during polling. For the existing
Compose instance, use host `localhost`, the published port (default `5432`), and
`production_analytics` for both database and user. Set the same password in
Compose's `.env` and the runner environment/secrets file.

Start the database and apply the repeatable schema from the repository root:

```bash
docker compose up -d timescaledb
docker compose exec -T timescaledb psql -U production_analytics -d production_analytics \
  -v ON_ERROR_STOP=1 < db/schema.sql
```

Wait until PostgreSQL is ready before applying the schema. Configure Grafana's
PostgreSQL data source to query `material_consumption_snapshots`, selecting
`timestamp` as time and `consumption_kg` as the cumulative value, filtered by
`calculation_id`, `machine_id`, and `production_order`. These are ordinary
PostgreSQL tables/indexes; no Timescale-specific features or hypertables are used.
The primary key deduplicates calculation/machine/order/timestamp (first write wins).
A short transaction opens and closes one synchronous connection per snapshot,
with 10-second connection and statement timeouts. Snapshot timestamps are poll
start times, not source-sample timestamps. Schema application is manual.
Bento 1 bentonite source and gate selection remain intentionally undefined pending
process validation; the generic integration core is unchanged and reusable.

## Peak-cycle detection

`PeakCycleDetector` is a pure calculation-domain component for roll length,
roll weight, and comparable sawtooth/batch signals. It retains the current
maximum and emits it only after the value is below
`drop_ratio * current_peak` continuously for `hold_seconds`. Configuration is
`min_peak`, `drop_ratio` (strictly between 0 and 1), non-negative
`hold_seconds`, and positive `max_sample_gap_seconds`. These are per-signal
configuration parameters, not globally fixed process constants. The hold uses elapsed
timestamps, never a sample count; an interval longer than the configured
maximum sample gap restarts a reset candidate,
so a timestamp gap alone is not evidence that a signal remained below threshold.
`max_sample_gap_seconds` is required rather than defaulted, so the source's
continuity assumption is explicit for each detector configuration.
Reset values need not approach zero (and can be negative), and peak magnitude
can vary materially between cycles. Equal timestamps are accepted in arrival
order but add no elapsed hold time; backwards timestamps are rejected.

Real-world validations use ignored local captures and are not part of the test
suite. K7 roll length (m), variable `bf81c547-dccf-4709-aee2-0f78366d1dfc`, is
the clean reference case: its 10-second capture produced exactly four plausible
peaks of about 49.98 m with `min_peak=10`, `drop_ratio=0.75`,
`hold_seconds=60`, and `max_sample_gap_seconds=20`. Run
`python scripts/validate_k7_roll_length.py` when that capture is available.

Bento 2 roll weight (kg), machine `6201f3de-b940-45d7-930c-1481c5ae9e1d`,
variable `059c0015-9aff-4270-a4ce-f21b83f99378`, is the more realistic
robustness case. Its observed 10-second signal has plateaus, reset values of
roughly 300–470 kg, and materially varying peak heights. With
`min_peak=800`, `drop_ratio=0.75`, `hold_seconds=60`, and
`max_sample_gap_seconds=20`, the extended capture produces 13 plausible
completed peaks, including a legitimate approximately 1195 kg cycle. Run
`python scripts/validate_bento2_roll_weight.py` when the ignored capture is
available. Neither capture should be added to version control or automated
tests.

The normal unit test suite mocks PostgreSQL and requires no live database.

See [PROJECT_KNOWLEDGE.md](PROJECT_KNOWLEDGE.md) for durable project context
and [docs/roadmap.md](docs/roadmap.md) for the implementation sequence.